##        IDjoueur    nom_du_joueur heure_connexion_joueur nom_du_jeu
##    1: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    2: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    3: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    4: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    5: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##   ---                                                             
## 5738: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5739: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5740: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5741: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5742: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
##       modeTest mise_first_1 action_de_jeu duree_tour_ms mise confiance
##    1:        1            1             1         34341    7       100
##    2:        1            1             2         24837    4        50
##    3:        1            1             3         31080    5        90
##    4:        0            1             1         20435    7       100
##    5:        0            1             2         43967    3        50
##   ---                                                                 
## 5738:        0            0            26          6363    7       100
## 5739:        0            0            27          6401    1        10
## 5740:        0            0            28          7363    2        30
## 5741:        0            0            29          6833    2        30
## 5742:        0            0            30          8730    1        20
##       difficulty gameDiff near_miss moutons_sauves moutons_tues score
##    1:       0.00     1.00         0              7            0     7
##    2:       0.10     2.00         0             11            0    11
##    3:       0.20     3.00         0             11            5     6
##    4:       0.00     1.00         0              7            0     7
##    5:       0.41     5.00         0             10            0    10
##   ---                                                                
## 5738:       0.20     3.60        17             60           59     1
## 5739:       0.87     8.96       -28             60           60     0
## 5740:       0.36     4.88       -10             62           60     2
## 5741:       0.55     6.40       -16             62           62     0
## 5742:       0.93     9.44        52             62           63    -1
##       gagnant          horodateur        prenomNom age sexe
##    1:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    2:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    3:       0 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    4:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    5:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##   ---                                                      
## 5738:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5739:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5740:       1 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5741:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5742:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
##       langueMaternelle niveauEtude
##    1:                1           7
##    2:                1           7
##    3:                1           7
##    4:                1           7
##    5:                1           7
##   ---                             
## 5738:                1           4
## 5739:                1           4
## 5740:                1           4
## 5741:                1           4
## 5742:                1           4
##                                              jeuxFav autoEffJoueur1
##    1:                                       pacman_              NA
##    2:                                       pacman_              NA
##    3:                                       pacman_              NA
##    4:                                       pacman_              NA
##    5:                                       pacman_              NA
##   ---                                                              
## 5738: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5739: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5740: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5741: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5742: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
##       autoEffJoueur2 autoEffJoueur3 autoEffJoueur4 autoEffJoueur5
##    1:             NA             NA             NA             NA
##    2:             NA             NA             NA             NA
##    3:             NA             NA             NA             NA
##    4:             NA             NA             NA             NA
##    5:             NA             NA             NA             NA
##   ---                                                            
## 5738:             NA             NA             NA             NA
## 5739:             NA             NA             NA             NA
## 5740:             NA             NA             NA             NA
## 5741:             NA             NA             NA             NA
## 5742:             NA             NA             NA             NA
##       autoEffJoueur6 autoEffJoueur7 autoEffJoueur8 autoEffJoueur9
##    1:             NA             NA             NA             NA
##    2:             NA             NA             NA             NA
##    3:             NA             NA             NA             NA
##    4:             NA             NA             NA             NA
##    5:             NA             NA             NA             NA
##   ---                                                            
## 5738:             NA             NA             NA             NA
## 5739:             NA             NA             NA             NA
## 5740:             NA             NA             NA             NA
## 5741:             NA             NA             NA             NA
## 5742:             NA             NA             NA             NA
##       autoEffJoueur10 loterie1 loterie2 loterie3 loterie4 loterie5
##    1:              NA        1        1        1        1        0
##    2:              NA        1        1        1        1        0
##    3:              NA        1        1        1        1        0
##    4:              NA        1        1        1        1        0
##    5:              NA        1        1        1        1        0
##   ---                                                             
## 5738:              NA        1        1        0        0        1
## 5739:              NA        1        1        0        0        1
## 5740:              NA        1        1        0        0        1
## 5741:              NA        1        1        0        0        1
## 5742:              NA        1        1        0        0        1
##       loterie6 loterie7 loterie8 loterie9 loterie10 profilJoueur8
##    1:        0        1        1        1         1             0
##    2:        0        1        1        1         1             0
##    3:        0        1        1        1         1             0
##    4:        0        1        1        1         1             0
##    5:        0        1        1        1         1             0
##   ---                                                            
## 5738:        1        1        1        1         1             0
## 5739:        1        1        1        1         1             0
## 5740:        1        1        1        1         1             0
## 5741:        1        1        1        1         1             0
## 5742:        1        1        1        1         1             0
##       play.video.games play.board.games play.money.games self.eff
##    1:              0.4              0.2              0.8       NA
##    2:              0.4              0.2              0.8       NA
##    3:              0.4              0.2              0.8       NA
##    4:              0.4              0.2              0.8       NA
##    5:              0.4              0.2              0.8       NA
##   ---                                                            
## 5738:              1.0              0.4              0.4       NA
## 5739:              1.0              0.4              0.4       NA
## 5740:              1.0              0.4              0.4       NA
## 5741:              1.0              0.4              0.4       NA
## 5742:              1.0              0.4              0.4       NA

Removing Outliers

## [1] "Outliers : 3qq8dp8jk, 79pn8m6v8, e58u3sinl, hudayxdge, w2x28nknu"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  1"
## [1] "Total number of outliers perceptive task:  1"
## [1] "Total number of outliers logical task:  3"

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2268.1   2290.2  -1130.0   2260.1     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9846 -0.7313  0.2308  0.7546  2.8895 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5178   0.7196  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.1555     0.1548  -7.464 8.42e-14 ***
## difficulty    3.0512     0.2019  15.113  < 2e-16 ***
## timeNorm     -0.3871     0.1728  -2.241   0.0251 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.488       
## timeNorm   -0.430 -0.167
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1881         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-0.9973563  
##  1st Qu.:-0.4243437  
##  Median :-0.1362009  
##  Mean   :-0.0003041  
##  3rd Qu.: 0.3781255  
##  Max.   : 1.6570924  
## [1] "Intercept: -1.16 8.4e-14 ***"
## [1] "Difficulty: 3.05 1.3e-51 ***"
## [1] "Time: -0.387 0.025 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2300"
##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1535.4   1557.4   -763.7   1527.4     1811 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2914 -0.4479  0.1164  0.3982  4.7670 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5772   0.7598  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.2311     0.1826 -12.217  < 2e-16 ***
## difficulty    7.0302     0.3250  21.631  < 2e-16 ***
## timeNorm     -1.0832     0.2369  -4.572 4.84e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.458       
## timeNorm   -0.385 -0.358
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1815 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.527169  
##  1st Qu.:-0.388916  
##  Median :-0.005975  
##  Mean   : 0.002363  
##  3rd Qu.: 0.374680  
##  Max.   : 1.350107  
## [1] "Intercept: -2.23 2.5e-34 ***"
## [1] "Difficulty: 7.03 9.1e-104 ***"
## [1] "Time: -1.08 4.8e-06 ***"
## [1] "R2 fixed: 0.55"
## [1] "R2 mixed: 0.62"
## [1] "Cross Val: 0.81"
## [1] "AIC: 1500"
##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1816.7   1838.8   -904.3   1808.7     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7618 -0.5239 -0.1972  0.5160  5.0573 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.067    1.033   
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.6536     0.1924  -8.596   <2e-16 ***
## difficulty    5.4305     0.2647  20.515   <2e-16 ***
## timeNorm     -2.0774     0.2224  -9.340   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.388       
## timeNorm   -0.276 -0.437
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1881         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.492039  
##  1st Qu.:-0.741161  
##  Median :-0.213560  
##  Mean   : 0.004668  
##  3rd Qu.: 0.599760  
##  Max.   : 2.373359  
## [1] "Intercept: -1.65 8.2e-18 ***"
## [1] "Difficulty: 5.43 1.6e-93 ***"
## [1] "Time: -2.08 9.6e-21 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.53"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1800"
##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0832, p-value = 0.2787
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1121498

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.27984, p-value = 0.7796
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02959975

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18429, p-value = 0.8538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01913758

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.92279, p-value = 0.3561
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09432639

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.40055, p-value = 0.6887
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04164333

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.83074, p-value = 0.4061
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08524489

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.5588, p-value = 0.0105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3482495 
## 
## [1] "self.eff.on.level.s 0.35 0.011 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.77294, p-value = 0.4396
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1034345

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2531, p-value = 0.2102
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1232133

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9255, p-value = 0.05417
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1918732 
## 
## [1] "risk.av.on.level.s 0.19 0.054 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0617, p-value = 0.2884
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1042971

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0129, p-value = 0.3111
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09643322
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0949, p-value = 0.03618
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2036664 
## 
## [1] "age.on.level.s 0.2 0.036 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2495, p-value = 0.2115
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1192254

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.3361, p-value = 0.01949
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.257113 
## 
## [1] "sexe.on.level.m -0.26 0.019 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0, p-value = 1
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau 
##   0

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18884, p-value = 0.8502
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02078441

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 223, p-value = 0.01897
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.85282846 -0.09534056
## sample estimates:
## difference in location 
##             -0.5051082 
## 
## [1] "sexe.on.level.m.2 -0.51 0.019 * mean(A): 0.16 mean(B): -0.32"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 333, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.3670949  0.4731302
## sample estimates:
## difference in location 
##          -0.0009246191

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 340, p-value = 0.8583
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7335260  0.5047401
## sample estimates:
## difference in location 
##            -0.02802612

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.62185, p-value = 0.534
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03720939

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.4464, p-value = 0.0005681
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2033235 
## 
## [1] "pbg.on.error -0.2 0.00057 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.44873, p-value = 0.6536
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02338143

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.23405, p-value = 0.8149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02130326

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.094374, p-value = 0.9248
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008754209

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.45433, p-value = 0.6496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04135338

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 4.1645, p-value = 3.12e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2646112 
## 
## [1] "sexe.on.error 0.26 3.1e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3699, p-value = 0.01779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2608393 
## 
## [1] "sexe.on.error.m 0.26 0.018 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.565, p-value = 0.01032
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2875846 
## 
## [1] "sexe.on.error.s 0.29 0.01 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2318, p-value = 0.02563
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2456339 
## 
## [1] "sexe.on.error.l 0.25 0.026 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4376, p-value = 3.143e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.04977679 0.13237866
## sample estimates:
## difference in location 
##             0.09299933 
## 
## [1] "sexe.on.error.2 0.093 3.1e-05 *** mean(A): -0.093 mean(B): 0.001"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 501, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01355287 0.15331497
## sample estimates:
## difference in location 
##             0.09290042 
## 
## [1] "sexe.on.error.m.2 0.093 0.017 * mean(A): -0.085 mean(B): 0.0073"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 476, p-value = 0.009655
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.02092227 0.15744127
## sample estimates:
## difference in location 
##             0.09796631 
## 
## [1] "sexe.on.error.s.2 0.098 0.0097 ** mean(A): -0.1 mean(B): -0.0014"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 481, p-value = 0.02523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.009389481 0.150561466
## sample estimates:
## difference in location 
##             0.09060751 
## 
## [1] "sexe.on.error.l.2 0.091 0.025 * mean(A): -0.091 mean(B): -0.0033"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.60676, p-value = 0.544
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03431688

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.12035, p-value = 0.9042
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01183404

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.11152, p-value = 0.9112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01111235

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.79275, p-value = 0.4279
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07787518

Self efficacy and subjective difficulty error

## Warning: Removed 84 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.9644, p-value = 0.003033
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2277125 
## 
## [1] "self.eff.on.error -0.23 0.003 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7653, p-value = 0.07751
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2402652 
## 
## [1] "self.eff.on.error -0.24 0.078 ."
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2240675 
## 
## [1] "self.eff.on.error -0.22 0.1 :("
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6401, p-value = 0.101
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2194829

OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0096 47     0.64 :(
##  2:      0.09375         0.0440 54     0.052 .
##  3:      0.15625         0.0045 58     0.91 :(
##  4:      0.21875         0.0260 58     0.27 :(
##  5:      0.28125         0.0044 57     0.98 :(
##  6:      0.34375        -0.0400 58     0.25 :(
##  7:      0.40625        -0.0400 58     0.23 :(
##  8:      0.46875        -0.0045 58     0.94 :(
##  9:      0.53125        -0.0190 58     0.54 :(
## 10:      0.59375        -0.0420 58     0.18 :(
## 11:      0.65625        -0.0370 58     0.31 :(
## 12:      0.71875        -0.1100 58 1.9e-05 ***
## 13:      0.78125        -0.1400 58 7.4e-08 ***
## 14:      0.84375        -0.2100 58 1.7e-09 ***
## 15:      0.90625        -0.1900 57   5e-11 ***
## 16:      0.96875        -0.1800 55 1.1e-10 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 47     0.64 :(
##  2: 54     0.052 .
##  3: 58     0.91 :(
##  4: 58     0.27 :(
##  5: 57     0.98 :(
##  6: 58     0.25 :(
##  7: 58     0.23 :(
##  8: 58     0.94 :(
##  9: 58     0.54 :(
## 10: 58     0.18 :(
## 11: 58     0.31 :(
## 12: 58 1.9e-05 ***
## 13: 58 7.4e-08 ***
## 14: 58 1.7e-09 ***
## 15: 57   5e-11 ***
## 16: 55 1.1e-10 ***
## [1] 56.8
## [1] 2.86

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0093 28     0.64 :(
##  2:      0.09375        -0.0220 31     0.41 :(
##  3:      0.15625        -0.0970 40     0.031 *
##  4:      0.21875        -0.0400 38      0.08 .
##  5:      0.28125        -0.0360 34     0.38 :(
##  6:      0.34375        -0.0580 38     0.17 :(
##  7:      0.40625        -0.0610 34     0.27 :(
##  8:      0.46875         0.0220 36     0.64 :(
##  9:      0.53125         0.0400 36     0.38 :(
## 10:      0.59375        -0.0580 38     0.22 :(
## 11:      0.65625        -0.0850 35     0.11 :(
## 12:      0.71875        -0.1500 35   5e-04 ***
## 13:      0.78125        -0.1500 33 0.00081 ***
## 14:      0.84375        -0.2700 25 0.00014 ***
## 15:      0.90625        -0.1900 23 2.7e-05 ***
## 16:      0.96875        -0.1500 19 0.00011 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28     0.64 :(
##  2: 31     0.41 :(
##  3: 40     0.031 *
##  4: 38      0.08 .
##  5: 34     0.38 :(
##  6: 38     0.17 :(
##  7: 34     0.27 :(
##  8: 36     0.64 :(
##  9: 36     0.38 :(
## 10: 38     0.22 :(
## 11: 35     0.11 :(
## 12: 35   5e-04 ***
## 13: 33 0.00081 ***
## 14: 25 0.00014 ***
## 15: 23 2.7e-05 ***
## 16: 19 0.00011 ***
## [1] 32.7
## [1] 6

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 27     0.29 :(
##  2:      0.09375         0.0780 37   0.0098 **
##  3:      0.15625        -0.0015 40     0.94 :(
##  4:      0.21875         0.0490 43     0.37 :(
##  5:      0.28125        -0.0130 43     0.78 :(
##  6:      0.34375        -0.0480 38      0.3 :(
##  7:      0.40625        -0.0420 43     0.38 :(
##  8:      0.46875        -0.0230 41     0.66 :(
##  9:      0.53125        -0.0310 39     0.55 :(
## 10:      0.59375        -0.0460 39     0.35 :(
## 11:      0.65625         0.0020 43     0.92 :(
## 12:      0.71875        -0.0580 41     0.058 .
## 13:      0.78125        -0.0990 44   0.0041 **
## 14:      0.84375        -0.1800 42 6.1e-06 ***
## 15:      0.90625        -0.1900 34 3.5e-07 ***
## 16:      0.96875        -0.1700 32   8e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 27     0.29 :(
##  2: 37   0.0098 **
##  3: 40     0.94 :(
##  4: 43     0.37 :(
##  5: 43     0.78 :(
##  6: 38      0.3 :(
##  7: 43     0.38 :(
##  8: 41     0.66 :(
##  9: 39     0.55 :(
## 10: 39     0.35 :(
## 11: 43     0.92 :(
## 12: 41     0.058 .
## 13: 44   0.0041 **
## 14: 42 6.1e-06 ***
## 15: 34 3.5e-07 ***
## 16: 32   8e-07 ***
## [1] 39.1
## [1] 4.69

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  4          NA
##  2:      0.09375         -0.094  9     0.53 :(
##  3:      0.15625          0.058 18     0.24 :(
##  4:      0.21875          0.110 20     0.059 .
##  5:      0.28125          0.150 22     0.064 .
##  6:      0.34375          0.130 21      0.04 *
##  7:      0.40625         -0.021 22     0.79 :(
##  8:      0.46875         -0.057 23     0.28 :(
##  9:      0.53125         -0.100 19     0.18 :(
## 10:      0.59375         -0.076 24     0.27 :(
## 11:      0.65625         -0.073 20     0.24 :(
## 12:      0.71875         -0.160 22   0.0063 **
## 13:      0.78125         -0.140 25   0.0015 **
## 14:      0.84375         -0.200 26 4.8e-05 ***
## 15:      0.90625         -0.160 26 8.5e-06 ***
## 16:      0.96875         -0.250 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.53 :(
##  2: 18     0.24 :(
##  3: 20     0.059 .
##  4: 22     0.064 .
##  5: 21      0.04 *
##  6: 22     0.79 :(
##  7: 23     0.28 :(
##  8: 19     0.18 :(
##  9: 24     0.27 :(
## 10: 20     0.24 :(
## 11: 22   0.0063 **
## 12: 25   0.0015 **
## 13: 26 4.8e-05 ***
## 14: 26 8.5e-06 ***
## 15: 25 1.3e-05 ***
## [1] 21.5
## [1] 4.26
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0940  8   0.71 :(
##  3:      0.15625        -0.0990 29   0.021 *
##  4:      0.21875        -0.0760 41   0.042 *
##  5:      0.28125        -0.0540 48    0.2 :(
##  6:      0.34375        -0.0400 50   0.22 :(
##  7:      0.40625        -0.0015 50    0.9 :(
##  8:      0.46875        -0.0022 54      1 :(
##  9:      0.53125         0.0400 52   0.17 :(
## 10:      0.59375         0.0063 51   0.82 :(
## 11:      0.65625         0.0220 52   0.79 :(
## 12:      0.71875        -0.0580 53   0.064 .
## 13:      0.78125        -0.0790 46   0.015 *
## 14:      0.84375        -0.0940 29   0.077 .
## 15:      0.90625        -0.0760 13 0.0012 **
## 16:      0.96875        -0.1100  6   0.031 *
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.71 :(
##  2: 29   0.021 *
##  3: 41   0.042 *
##  4: 48    0.2 :(
##  5: 50   0.22 :(
##  6: 50    0.9 :(
##  7: 54      1 :(
##  8: 52   0.17 :(
##  9: 51   0.82 :(
## 10: 52   0.79 :(
## 11: 53   0.064 .
## 12: 46   0.015 *
## 13: 29   0.077 .
## 14: 13 0.0012 **
## 15:  6   0.031 *
## [1] 38.8
## [1] 17.3
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375         -0.094  8 0.71 :(
##  3:      0.15625         -0.099 24 0.023 *
##  4:      0.21875         -0.066 25 0.067 .
##  5:      0.28125         -0.043 25 0.31 :(
##  6:      0.34375         -0.040 25 0.32 :(
##  7:      0.40625          0.040 24  0.4 :(
##  8:      0.46875          0.067 24 0.12 :(
##  9:      0.53125          0.110 23 0.021 *
## 10:      0.59375          0.120 22 0.043 *
## 11:      0.65625          0.029 22 0.52 :(
## 12:      0.71875         -0.040 21 0.094 .
## 13:      0.78125         -0.067 15 0.32 :(
## 14:      0.84375             NA  2      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  8 0.71 :(
##  2: 24 0.023 *
##  3: 25 0.067 .
##  4: 25 0.31 :(
##  5: 25 0.32 :(
##  6: 24  0.4 :(
##  7: 24 0.12 :(
##  8: 23 0.021 *
##  9: 22 0.043 *
## 10: 22 0.52 :(
## 11: 21 0.094 .
## 12: 15 0.32 :(
## [1] 21.5
## [1] 5.07
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  5      NA
##  4:      0.21875        -0.0045 16 0.41 :(
##  5:      0.28125        -0.0670 23 0.51 :(
##  6:      0.34375        -0.0580 24  0.3 :(
##  7:      0.40625        -0.0320 25 0.73 :(
##  8:      0.46875        -0.0400 25  0.5 :(
##  9:      0.53125         0.0220 25 0.69 :(
## 10:      0.59375        -0.0220 22  0.9 :(
## 11:      0.65625         0.0410 23 0.66 :(
## 12:      0.71875         0.0310 25 0.65 :(
## 13:      0.78125        -0.0670 25 0.13 :(
## 14:      0.84375        -0.0940 20 0.15 :(
## 15:      0.90625             NA  6      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 16 0.41 :(
##  2: 23 0.51 :(
##  3: 24  0.3 :(
##  4: 25 0.73 :(
##  5: 25  0.5 :(
##  6: 25 0.69 :(
##  7: 22  0.9 :(
##  8: 23 0.66 :(
##  9: 25 0.65 :(
## 10: 25 0.13 :(
## 11: 20 0.15 :(
## [1] 23
## [1] 2.83
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         -0.150 5 0.28 :(
##  9:      0.53125         -0.220 4 0.38 :(
## 10:      0.59375         -0.290 7 0.078 .
## 11:      0.65625         -0.130 7 0.35 :(
## 12:      0.71875         -0.260 7 0.047 *
## 13:      0.78125         -0.160 6 0.16 :(
## 14:      0.84375         -0.120 7  0.2 :(
## 15:      0.90625         -0.081 7 0.022 *
## 16:      0.96875         -0.110 6 0.031 *
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  5 0.28 :(
## 2:  4 0.38 :(
## 3:  7 0.078 .
## 4:  7 0.35 :(
## 5:  7 0.047 *
## 6:  6 0.16 :(
## 7:  7  0.2 :(
## 8:  7 0.022 *
## 9:  6 0.031 *
## [1] 6.22
## [1] 1.09
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 32     0.034 *
##  2:      0.09375        -0.0065 48     0.64 :(
##  3:      0.15625        -0.0970 51   0.0069 **
##  4:      0.21875        -0.0760 47   0.0011 **
##  5:      0.28125        -0.0670 46      0.1 :(
##  6:      0.34375        -0.1300 41     0.063 .
##  7:      0.40625        -0.1200 44     0.053 .
##  8:      0.46875        -0.1100 42     0.036 *
##  9:      0.53125        -0.1700 34   0.0079 **
## 10:      0.59375        -0.2400 37 0.00062 ***
## 11:      0.65625        -0.1100 40     0.12 :(
## 12:      0.71875        -0.1700 46 0.00063 ***
## 13:      0.78125        -0.1700 42   0.0042 **
## 14:      0.84375        -0.1700 46   9e-06 ***
## 15:      0.90625        -0.1600 53 1.9e-10 ***
## 16:      0.96875        -0.1400 55 9.4e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.034 *
##  2: 48     0.64 :(
##  3: 51   0.0069 **
##  4: 47   0.0011 **
##  5: 46      0.1 :(
##  6: 41     0.063 .
##  7: 44     0.053 .
##  8: 42     0.036 *
##  9: 34   0.0079 **
## 10: 37 0.00062 ***
## 11: 40     0.12 :(
## 12: 46 0.00063 ***
## 13: 42   0.0042 **
## 14: 46   9e-06 ***
## 15: 53 1.9e-10 ***
## 16: 55 9.4e-11 ***
## [1] 44
## [1] 6.4

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0044 18        1 :(
##  2:      0.09375        -0.0530 19     0.033 *
##  3:      0.15625        -0.1600 17     0.048 *
##  4:      0.21875        -0.1500 13     0.019 *
##  5:      0.28125        -0.1000 13     0.29 :(
##  6:      0.34375        -0.1300 13     0.024 *
##  7:      0.40625        -0.2600 14    0.008 **
##  8:      0.46875        -0.1100 16     0.22 :(
##  9:      0.53125        -0.2100 14     0.044 *
## 10:      0.59375        -0.4400 11    0.005 **
## 11:      0.65625        -0.1600 13     0.069 .
## 12:      0.71875        -0.1800 16   0.0065 **
## 13:      0.78125        -0.2800 13      0.03 *
## 14:      0.84375        -0.1700 15     0.011 *
## 15:      0.90625        -0.1400 18 0.00018 ***
## 16:      0.96875        -0.1500 19 0.00011 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 18        1 :(
##  2: 19     0.033 *
##  3: 17     0.048 *
##  4: 13     0.019 *
##  5: 13     0.29 :(
##  6: 13     0.024 *
##  7: 14    0.008 **
##  8: 16     0.22 :(
##  9: 14     0.044 *
## 10: 11    0.005 **
## 11: 13     0.069 .
## 12: 16   0.0065 **
## 13: 13      0.03 *
## 14: 15     0.011 *
## 15: 18 0.00018 ***
## 16: 19 0.00011 ***
## [1] 15.1
## [1] 2.5

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA 14          NA
##  2:      0.09375          0.021 26      0.4 :(
##  3:      0.15625         -0.097 26     0.026 *
##  4:      0.21875         -0.110 25   0.0067 **
##  5:      0.28125         -0.100 24     0.21 :(
##  6:      0.34375         -0.058 20     0.61 :(
##  7:      0.40625         -0.085 22     0.45 :(
##  8:      0.46875         -0.110 20     0.16 :(
##  9:      0.53125         -0.150 16     0.15 :(
## 10:      0.59375         -0.170 21     0.14 :(
## 11:      0.65625         -0.160 21     0.55 :(
## 12:      0.71875         -0.076 22     0.026 *
## 13:      0.78125         -0.120 21     0.087 .
## 14:      0.84375         -0.170 24   0.0026 **
## 15:      0.90625         -0.160 26 8.2e-06 ***
## 16:      0.96875         -0.150 27 5.6e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 26      0.4 :(
##  2: 26     0.026 *
##  3: 25   0.0067 **
##  4: 24     0.21 :(
##  5: 20     0.61 :(
##  6: 22     0.45 :(
##  7: 20     0.16 :(
##  8: 16     0.15 :(
##  9: 21     0.14 :(
## 10: 21     0.55 :(
## 11: 22     0.026 *
## 12: 21     0.087 .
## 13: 24   0.0026 **
## 14: 26 8.2e-06 ***
## 15: 27 5.6e-06 ***
## [1] 22.7
## [1] 3.03
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n      pval
##  1:      0.03125             NA 0        NA
##  2:      0.09375             NA 3        NA
##  3:      0.15625          0.020 8   0.94 :(
##  4:      0.21875          0.013 9   0.81 :(
##  5:      0.28125         -0.013 9      1 :(
##  6:      0.34375         -0.078 8   0.72 :(
##  7:      0.40625          0.064 8   0.53 :(
##  8:      0.46875         -0.088 6   0.53 :(
##  9:      0.53125         -0.230 4   0.36 :(
## 10:      0.59375         -0.170 5   0.42 :(
## 11:      0.65625          0.022 6   0.83 :(
## 12:      0.71875         -0.130 8   0.62 :(
## 13:      0.78125         -0.077 8   0.53 :(
## 14:      0.84375         -0.120 7   0.075 .
## 15:      0.90625         -0.160 9 0.0086 **
## 16:      0.96875         -0.120 9 0.0091 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.94 :(
##  2:  9   0.81 :(
##  3:  9      1 :(
##  4:  8   0.72 :(
##  5:  8   0.53 :(
##  6:  6   0.53 :(
##  7:  4   0.36 :(
##  8:  5   0.42 :(
##  9:  6   0.83 :(
## 10:  8   0.62 :(
## 11:  8   0.53 :(
## 12:  7   0.075 .
## 13:  9 0.0086 **
## 14:  9 0.0091 **
## [1] 7.43
## [1] 1.6
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0005 38     0.78 :(
##  2:      0.09375         0.0970 43      0.01 *
##  3:      0.15625         0.0940 48      0.04 *
##  4:      0.21875         0.1600 50   0.0046 **
##  5:      0.28125         0.1500 49     0.015 *
##  6:      0.34375         0.0850 41      0.08 .
##  7:      0.40625         0.0220 47     0.77 :(
##  8:      0.46875        -0.0400 47     0.64 :(
##  9:      0.53125         0.0160 45     0.73 :(
## 10:      0.59375        -0.0370 46      0.6 :(
## 11:      0.65625        -0.0490 42     0.32 :(
## 12:      0.71875        -0.1500 41 0.00057 ***
## 13:      0.78125        -0.1400 53 0.00026 ***
## 14:      0.84375        -0.2600 52 1.4e-08 ***
## 15:      0.90625        -0.2400 42 1.7e-08 ***
## 16:      0.96875        -0.3300 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.78 :(
##  2: 43      0.01 *
##  3: 48      0.04 *
##  4: 50   0.0046 **
##  5: 49     0.015 *
##  6: 41      0.08 .
##  7: 47     0.77 :(
##  8: 47     0.64 :(
##  9: 45     0.73 :(
## 10: 46      0.6 :(
## 11: 42     0.32 :(
## 12: 41 0.00057 ***
## 13: 53 0.00026 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.7e-08 ***
## 16: 29 2.7e-06 ***
## [1] 44.6
## [1] 5.93

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125          0.012 16 0.51 :(
##  2:      0.09375          0.044 16 0.37 :(
##  3:      0.15625          0.058 15  0.8 :(
##  4:      0.21875          0.031 14  0.8 :(
##  5:      0.28125         -0.048 11 0.69 :(
##  6:      0.34375         -0.082 13 0.67 :(
##  7:      0.40625         -0.085  9 0.72 :(
##  8:      0.46875         -0.040 14 0.49 :(
##  9:      0.53125          0.040 14 0.45 :(
## 10:      0.59375         -0.170 13 0.18 :(
## 11:      0.65625         -0.160 11 0.12 :(
## 12:      0.71875         -0.280 11 0.014 *
## 13:      0.78125         -0.280 15 0.033 *
## 14:      0.84375         -0.390 12 0.011 *
## 15:      0.90625         -0.410  7 0.022 *
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 16 0.51 :(
##  2: 16 0.37 :(
##  3: 15  0.8 :(
##  4: 14  0.8 :(
##  5: 11 0.69 :(
##  6: 13 0.67 :(
##  7:  9 0.72 :(
##  8: 14 0.49 :(
##  9: 14 0.45 :(
## 10: 13 0.18 :(
## 11: 11 0.12 :(
## 12: 11 0.014 *
## 13: 15 0.033 *
## 14: 12 0.011 *
## 15:  7 0.022 *
## [1] 12.7
## [1] 2.58
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.016 18     0.75 :(
##  2:      0.09375          0.150 21   0.0031 **
##  3:      0.15625          0.130 22     0.048 *
##  4:      0.21875          0.190 21     0.016 *
##  5:      0.28125          0.110 21     0.13 :(
##  6:      0.34375          0.085 14     0.23 :(
##  7:      0.40625          0.022 20     0.42 :(
##  8:      0.46875          0.140 17     0.32 :(
##  9:      0.53125          0.040 16     0.78 :(
## 10:      0.59375         -0.022 17     0.96 :(
## 11:      0.65625         -0.013 18     0.79 :(
## 12:      0.71875         -0.150 17      0.2 :(
## 13:      0.78125         -0.140 20     0.12 :(
## 14:      0.84375         -0.200 21 0.00015 ***
## 15:      0.90625         -0.200 16 0.00048 ***
## 16:      0.96875         -0.400 10   0.0057 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 18     0.75 :(
##  2: 21   0.0031 **
##  3: 22     0.048 *
##  4: 21     0.016 *
##  5: 21     0.13 :(
##  6: 14     0.23 :(
##  7: 20     0.42 :(
##  8: 17     0.32 :(
##  9: 16     0.78 :(
## 10: 17     0.96 :(
## 11: 18     0.79 :(
## 12: 17      0.2 :(
## 13: 20     0.12 :(
## 14: 21 0.00015 ***
## 15: 16 0.00048 ***
## 16: 10   0.0057 **
## [1] 18.1
## [1] 3.17

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  4          NA
##  2:      0.09375             NA  6          NA
##  3:      0.15625          0.082 11     0.26 :(
##  4:      0.21875          0.210 15     0.056 .
##  5:      0.28125          0.220 17     0.017 *
##  6:      0.34375          0.230 14     0.032 *
##  7:      0.40625          0.022 18     0.97 :(
##  8:      0.46875         -0.040 16     0.34 :(
##  9:      0.53125         -0.031 15     0.93 :(
## 10:      0.59375          0.085 16     0.66 :(
## 11:      0.65625          0.022 13     0.83 :(
## 12:      0.71875         -0.150 13     0.025 *
## 13:      0.78125         -0.120 18     0.011 *
## 14:      0.84375         -0.220 19   0.0013 **
## 15:      0.90625         -0.190 19 0.00014 ***
## 16:      0.96875         -0.300 19 0.00014 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.26 :(
##  2: 15     0.056 .
##  3: 17     0.017 *
##  4: 14     0.032 *
##  5: 18     0.97 :(
##  6: 16     0.34 :(
##  7: 15     0.93 :(
##  8: 16     0.66 :(
##  9: 13     0.83 :(
## 10: 13     0.025 *
## 11: 18     0.011 *
## 12: 19   0.0013 **
## 13: 19 0.00014 ***
## 14: 19 0.00014 ***
## [1] 15.9
## [1] 2.56
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85425  -0.20543   0.02783   0.20243   0.70750  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006664   0.018685  -0.357 0.721400    
## timeNorm     0.016339   0.020930   0.781 0.435103    
## obj.diff    -0.094710   0.028659  -3.305 0.000969 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07620175)
## 
##     Null deviance: 143.99  on 1880  degrees of freedom
## Residual deviance: 143.11  on 1878  degrees of freedom
## AIC: 500.65
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78155  -0.13780  -0.01151   0.12280   0.83005  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006795   0.014205  -0.478    0.632    
## timeNorm     0.011968   0.020729   0.577    0.564    
## obj.diff    -0.205459   0.018132 -11.331   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0706054)
## 
##     Null deviance: 137.08  on 1814  degrees of freedom
## Residual deviance: 127.94  on 1812  degrees of freedom
## AIC: 344.82
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71153  -0.24157   0.00719   0.24129   0.65825  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17431    0.01800   9.684   <2e-16 ***
## timeNorm     0.02107    0.02417   0.872    0.384    
## obj.diff    -0.46661    0.02329 -20.037   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1007759)
## 
##     Null deviance: 230.47  on 1880  degrees of freedom
## Residual deviance: 189.26  on 1878  degrees of freedom
## AIC: 1026.4
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.3918129     0.4457102 -0.05543814 342 0.0017 **
##  2:      4.5      0.4954052     0.5624859 -0.05783222 171 0.0052 **
##  3:      7.5      0.4928989     0.5357049 -0.03693287 171   0.077 .
##  4:     10.5      0.5071011     0.5362058 -0.02578583 171   0.23 :(
##  5:     13.5      0.4519632     0.5133937 -0.05576376 171 0.0061 **
##  6:     16.5      0.4995823     0.5320036 -0.01539779 171   0.46 :(
##  7:     19.5      0.4803676     0.5358363 -0.04608428 171   0.025 *
##  8:     22.5      0.4527987     0.4961373 -0.03638516 171   0.091 .
##  9:     25.5      0.4536341     0.4868060 -0.02527202 171   0.27 :(
## 10:     28.5      0.4243943     0.4657574 -0.03934980 171   0.074 .
##     time  error.diff shapes
##  1:  1.5 -0.05543814     24
##  2:  4.5 -0.05783222     24
##  3:  7.5 -0.03693287     16
##  4: 10.5 -0.02578583     16
##  5: 13.5 -0.05576376     24
##  6: 16.5 -0.01539779     16
##  7: 19.5 -0.04608428     24
##  8: 22.5 -0.03638516     16
##  9: 25.5 -0.02527202     16
## 10: 28.5 -0.03934980     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2761905     0.3121174 -0.08025653 330 2.2e-05 ***
##  2:      4.5      0.5194805     0.6623901 -0.12246157 165 6.5e-13 ***
##  3:      7.5      0.4259740     0.5756691 -0.12748326 165 6.7e-13 ***
##  4:     10.5      0.4658009     0.6169890 -0.12563962 165 5.6e-14 ***
##  5:     13.5      0.4251082     0.5882784 -0.13475627 165 4.2e-16 ***
##  6:     16.5      0.4025974     0.5480044 -0.12850300 165 1.9e-12 ***
##  7:     19.5      0.4666667     0.5706900 -0.09391859 165 2.3e-08 ***
##  8:     22.5      0.4311688     0.5568448 -0.12173493 165 1.6e-10 ***
##  9:     25.5      0.4891775     0.5635905 -0.08515151 165 7.1e-08 ***
## 10:     28.5      0.4649351     0.5525507 -0.08873994 165 1.2e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.08025653     24
##  2:  4.5 -0.12246157     24
##  3:  7.5 -0.12748326     24
##  4: 10.5 -0.12563962     24
##  5: 13.5 -0.13475627     24
##  6: 16.5 -0.12850300     24
##  7: 19.5 -0.09391859     24
##  8: 22.5 -0.12173493     24
##  9: 25.5 -0.08515151     24
## 10: 28.5 -0.08873994     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.3483709     0.3515160 -0.0257788254 342     0.26 :(
##  2:      4.5      0.5037594     0.6513076 -0.1432600132 171 4.6e-08 ***
##  3:      7.5      0.5037594     0.5682979 -0.0702473202 171   0.0057 **
##  4:     10.5      0.4970760     0.5388474 -0.0530333212 171      0.04 *
##  5:     13.5      0.4761905     0.5225795 -0.0457087630 171     0.099 .
##  6:     16.5      0.4820384     0.5042410 -0.0325739632 171     0.21 :(
##  7:     19.5      0.4185464     0.4415088 -0.0319575055 171     0.25 :(
##  8:     22.5      0.3918129     0.4078173 -0.0213488721 171     0.43 :(
##  9:     25.5      0.3851295     0.3856125 -0.0035008941 171      0.9 :(
## 10:     28.5      0.3792815     0.3513216 -0.0006985616 171     0.98 :(
##     time    error.diff shapes
##  1:  1.5 -0.0257788254     16
##  2:  4.5 -0.1432600132     24
##  3:  7.5 -0.0702473202     24
##  4: 10.5 -0.0530333212     24
##  5: 13.5 -0.0457087630     16
##  6: 16.5 -0.0325739632     16
##  7: 19.5 -0.0319575055     16
##  8: 22.5 -0.0213488721     16
##  9: 25.5 -0.0035008941     16
## 10: 28.5 -0.0006985616     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7283  -0.2728   0.1294   0.2016   0.5850  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21505    0.02876   7.477  1.5e-13 ***
## timeNorm     0.01392    0.03098   0.449    0.653    
## obj.diff    -0.48637    0.03430 -14.179  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1024864)
## 
##     Null deviance: 138.68  on 1154  degrees of freedom
## Residual deviance: 118.06  on 1152  degrees of freedom
## AIC: 651.62
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79280  -0.20411   0.04097   0.20291   0.72423  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06731    0.01468   4.585 4.76e-06 ***
## timeNorm     0.04344    0.01935   2.245   0.0248 *  
## obj.diff    -0.27646    0.02013 -13.731  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08439343)
## 
##     Null deviance: 221.99  on 2441  degrees of freedom
## Residual deviance: 205.84  on 2439  degrees of freedom
## AIC: 897.82
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73813  -0.18312  -0.06486   0.18831   0.79768  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.03629    0.01418   2.558   0.0106 *  
## timeNorm     0.01309    0.02010   0.651   0.5148    
## obj.diff    -0.23787    0.02195 -10.836   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07396167)
## 
##     Null deviance: 154.93  on 1979  degrees of freedom
## Residual deviance: 146.22  on 1977  degrees of freedom
## AIC: 467.66
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4795918     0.5376740 -0.06317999 210     0.022 *
##  2:      4.5      0.6340136     0.8056827 -0.13614192 105 4.3e-08 ***
##  3:      7.5      0.6040816     0.7450231 -0.12356755 105 1.6e-05 ***
##  4:     10.5      0.6108844     0.7038259 -0.09260169 105    0.003 **
##  5:     13.5      0.5931973     0.7173646 -0.10966239 105   7e-05 ***
##  6:     16.5      0.5986395     0.7274973 -0.10609599 105   9e-05 ***
##  7:     19.5      0.5687075     0.6685055 -0.08310054 105     0.012 *
##  8:     22.5      0.5687075     0.6739166 -0.08713138 105   0.0027 **
##  9:     25.5      0.5238095     0.6393948 -0.10457762 105 0.00057 ***
## 10:     28.5      0.5523810     0.5934189 -0.04289068 105     0.073 .
##     time  error.diff shapes
##  1:  1.5 -0.06317999     24
##  2:  4.5 -0.13614192     24
##  3:  7.5 -0.12356755     24
##  4: 10.5 -0.09260169     24
##  5: 13.5 -0.10966239     24
##  6: 16.5 -0.10609599     24
##  7: 19.5 -0.08310054     24
##  8: 22.5 -0.08713138     24
##  9: 25.5 -0.10457762     24
## 10: 28.5 -0.04289068     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3407336     0.3611307 -0.04003289 444     0.027 *
##  2:      4.5      0.5276705     0.6559888 -0.11981339 222 2.2e-10 ***
##  3:      7.5      0.4800515     0.5451680 -0.06872960 222 0.00013 ***
##  4:     10.5      0.5038610     0.5839184 -0.07900619 222 4.3e-05 ***
##  5:     13.5      0.4781210     0.5578799 -0.07753452 222 9.1e-05 ***
##  6:     16.5      0.4768340     0.5217832 -0.04662507 222     0.023 *
##  7:     19.5      0.4961390     0.5390272 -0.04371726 222     0.013 *
##  8:     22.5      0.4118404     0.4689708 -0.06669797 222 0.00091 ***
##  9:     25.5      0.4671815     0.4777792 -0.02515232 222     0.25 :(
## 10:     28.5      0.4665380     0.4859167 -0.03352356 222     0.093 .
##     time  error.diff shapes
##  1:  1.5 -0.04003289     24
##  2:  4.5 -0.11981339     24
##  3:  7.5 -0.06872960     24
##  4: 10.5 -0.07900619     24
##  5: 13.5 -0.07753452     24
##  6: 16.5 -0.04662507     24
##  7: 19.5 -0.04371726     24
##  8: 22.5 -0.06669797     24
##  9: 25.5 -0.02515232     16
## 10: 28.5 -0.03352356     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2563492     0.2844347 -0.05793583 360   3e-04 ***
##  2:      4.5      0.4047619     0.4812604 -0.08091450 180 0.00048 ***
##  3:      7.5      0.3928571     0.4695287 -0.07654093 180 0.00061 ***
##  4:     10.5      0.4031746     0.4561427 -0.06203485 180   0.0018 **
##  5:     13.5      0.3357143     0.4169152 -0.08655490 180 2.7e-05 ***
##  6:     16.5      0.3642857     0.4188636 -0.05961419 180    0.004 **
##  7:     19.5      0.3380952     0.3968486 -0.06239372 180   0.0014 **
##  8:     22.5      0.3579365     0.3976826 -0.04670165 180     0.029 *
##  9:     25.5      0.3634921     0.3831808 -0.02729630 180     0.21 :(
## 10:     28.5      0.2920635     0.3372715 -0.05901936 180   0.0027 **
##     time  error.diff shapes
##  1:  1.5 -0.05793583     24
##  2:  4.5 -0.08091450     24
##  3:  7.5 -0.07654093     24
##  4: 10.5 -0.06203485     24
##  5: 13.5 -0.08655490     24
##  6: 16.5 -0.05961419     24
##  7: 19.5 -0.06239372     24
##  8: 22.5 -0.04670165     24
##  9: 25.5 -0.02729630     16
## 10: 28.5 -0.05901936     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78307  -0.20030   0.09965   0.20287   0.56643  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.22999    0.11949  -1.925   0.0555 .
## timeNorm    -0.03598    0.06998  -0.514   0.6077  
## obj.diff     0.08283    0.15050   0.550   0.5826  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1038657)
## 
##     Null deviance: 23.736  on 230  degrees of freedom
## Residual deviance: 23.681  on 228  degrees of freedom
## AIC: 137.39
## 
## Number of Fisher Scoring iterations: 2
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5374150     0.7170177 -0.17337940 42     0.011 *
##  2:      4.5      0.6122449     0.7946114 -0.14180958 21   0.0063 **
##  3:      7.5      0.6326531     0.7649789 -0.07966557 21     0.038 *
##  4:     10.5      0.6394558     0.7869246 -0.09982316 21    0.009 **
##  5:     13.5      0.6190476     0.8120284 -0.09702938 21     0.013 *
##  6:     16.5      0.5102041     0.7887369 -0.26023913 21   0.0049 **
##  7:     19.5      0.5442177     0.7250289 -0.16961596 21      0.05 .
##  8:     22.5      0.6462585     0.7637626 -0.03896849 21     0.49 :(
##  9:     25.5      0.5578231     0.8157609 -0.26561302 21 0.00072 ***
## 10:     28.5      0.5986395     0.7674702 -0.09317669 21      0.06 .
##     time  error.diff shapes
##  1:  1.5 -0.17337940     24
##  2:  4.5 -0.14180958     24
##  3:  7.5 -0.07966557     24
##  4: 10.5 -0.09982316     24
##  5: 13.5 -0.09702938     24
##  6: 16.5 -0.26023913     24
##  7: 19.5 -0.16961596     16
##  8: 22.5 -0.03896849     16
##  9: 25.5 -0.26561302     24
## 10: 28.5 -0.09317669     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79768  -0.22927   0.04496   0.19205   0.68904  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04646    0.03146  -1.477    0.140
## timeNorm     0.01656    0.03145   0.527    0.599
## obj.diff    -0.01154    0.04892  -0.236    0.814
## 
## (Dispersion parameter for gaussian family taken to be 0.07542452)
## 
##     Null deviance: 62.024  on 824  degrees of freedom
## Residual deviance: 61.999  on 822  degrees of freedom
## AIC: 213.93
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.4219048     0.4703926 -0.04882302 150 0.077 .
##  2:      4.5      0.5314286     0.6181213 -0.07545117  75  0.02 *
##  3:      7.5      0.5028571     0.5405554 -0.02915190  75 0.35 :(
##  4:     10.5      0.5333333     0.5682867 -0.03243828  75 0.34 :(
##  5:     13.5      0.5200000     0.5516441 -0.02674953  75 0.43 :(
##  6:     16.5      0.5428571     0.5685882 -0.02122707  75 0.62 :(
##  7:     19.5      0.5447619     0.5794923 -0.02965771  75 0.39 :(
##  8:     22.5      0.4380952     0.5231952 -0.09195474  75 0.015 *
##  9:     25.5      0.4819048     0.5079792 -0.03043348  75 0.41 :(
## 10:     28.5      0.4819048     0.5148979 -0.03878182  75 0.31 :(
##     time  error.diff shapes
##  1:  1.5 -0.04882302     16
##  2:  4.5 -0.07545117     24
##  3:  7.5 -0.02915190     16
##  4: 10.5 -0.03243828     16
##  5: 13.5 -0.02674953     16
##  6: 16.5 -0.02122707     16
##  7: 19.5 -0.02965771     16
##  8: 22.5 -0.09195474     24
##  9: 25.5 -0.03043348     16
## 10: 28.5 -0.03878182     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80335  -0.18155  -0.01888   0.19399   0.72918  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.06141    0.02493  -2.463   0.0140 *
## timeNorm     0.03220    0.02896   1.112   0.2664  
## obj.diff     0.08952    0.04587   1.952   0.0513 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06387276)
## 
##     Null deviance: 52.815  on 824  degrees of freedom
## Residual deviance: 52.503  on 822  degrees of freedom
## AIC: 76.782
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.3209524     0.3450617 -0.03697026 150 0.13 :(
##  2:      4.5      0.4266667     0.4418554 -0.01357897  75 0.61 :(
##  3:      7.5      0.4438095     0.4666577 -0.02092597  75 0.44 :(
##  4:     10.5      0.4438095     0.4339237  0.01170254  75 0.74 :(
##  5:     13.5      0.3371429     0.3915256 -0.05898709  75 0.041 *
##  6:     16.5      0.4533333     0.4235337  0.02549517  75 0.27 :(
##  7:     19.5      0.3980952     0.4392064 -0.03752952  75 0.23 :(
##  8:     22.5      0.4133333     0.3941442  0.01243771  75 0.56 :(
##  9:     25.5      0.3961905     0.3735255  0.02575106  75 0.45 :(
## 10:     28.5      0.3180952     0.3321373 -0.01719639  75 0.56 :(
##     time  error.diff shapes
##  1:  1.5 -0.03697026     16
##  2:  4.5 -0.01357897     16
##  3:  7.5 -0.02092597     16
##  4: 10.5  0.01170254     16
##  5: 13.5 -0.05898709     24
##  6: 16.5  0.02549517     16
##  7: 19.5 -0.03752952     16
##  8: 22.5  0.01243771     16
##  9: 25.5  0.02575106     16
## 10: 28.5 -0.01719639     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78743  -0.20369   0.06208   0.11509   0.68210  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.09528    0.04255   2.239   0.0259 *  
## timeNorm     0.02131    0.05414   0.394   0.6942    
## obj.diff    -0.30196    0.05180  -5.830 1.46e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07877575)
## 
##     Null deviance: 25.854  on 296  degrees of freedom
## Residual deviance: 23.160  on 294  degrees of freedom
## AIC: 93.113
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4232804     0.4183202 -0.01635641 54     0.73 :(
##  2:      4.5      0.6613757     0.7447332 -0.09966346 27     0.052 .
##  3:      7.5      0.5502646     0.7363338 -0.15936527 27   0.0042 **
##  4:     10.5      0.5661376     0.7314530 -0.14016274 27    0.007 **
##  5:     13.5      0.5767196     0.7392145 -0.12926885 27 0.00092 ***
##  6:     16.5      0.5555556     0.6825762 -0.12651064 27   0.0065 **
##  7:     19.5      0.6349206     0.7056857 -0.09010470 27     0.26 :(
##  8:     22.5      0.6507937     0.7325326 -0.12078171 27      0.03 *
##  9:     25.5      0.5132275     0.6521805 -0.13254820 27 0.00018 ***
## 10:     28.5      0.6031746     0.6002956 -0.03415719 27     0.51 :(
##     time  error.diff shapes
##  1:  1.5 -0.01635641     16
##  2:  4.5 -0.09966346     16
##  3:  7.5 -0.15936527     24
##  4: 10.5 -0.14016274     24
##  5: 13.5 -0.12926885     24
##  6: 16.5 -0.12651064     24
##  7: 19.5 -0.09010470     16
##  8: 22.5 -0.12078171     24
##  9: 25.5 -0.13254820     24
## 10: 28.5 -0.03415719     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79017  -0.14265   0.02575   0.12081   0.79367  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0007053  0.0208951  -0.034    0.973    
## timeNorm     0.0108023  0.0298646   0.362    0.718    
## obj.diff    -0.2026250  0.0265323  -7.637 5.75e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07217403)
## 
##     Null deviance: 68.332  on 890  degrees of freedom
## Residual deviance: 64.091  on 888  degrees of freedom
## AIC: 191.39
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2980600     0.3201704 -0.07870225 162      0.05 .
##  2:      4.5      0.5608466     0.7228117 -0.12948975  81 5.4e-08 ***
##  3:      7.5      0.4338624     0.5731369 -0.12792864  81 2.7e-06 ***
##  4:     10.5      0.4779541     0.6353644 -0.12983440  81 4.6e-07 ***
##  5:     13.5      0.4391534     0.6134256 -0.14621999  81 1.8e-09 ***
##  6:     16.5      0.4179894     0.5473494 -0.12095073  81 2.6e-05 ***
##  7:     19.5      0.4973545     0.5800608 -0.08629896  81 0.00044 ***
##  8:     22.5      0.3932981     0.5224855 -0.13022568  81 3.7e-05 ***
##  9:     25.5      0.5167549     0.5725870 -0.07754871  81 0.00026 ***
## 10:     28.5      0.4902998     0.5806617 -0.08946090  81 5.3e-06 ***
##     time  error.diff shapes
##  1:  1.5 -0.07870225     24
##  2:  4.5 -0.12948975     24
##  3:  7.5 -0.12792864     24
##  4: 10.5 -0.12983440     24
##  5: 13.5 -0.14621999     24
##  6: 16.5 -0.12095073     24
##  7: 19.5 -0.08629896     24
##  8: 22.5 -0.13022568     24
##  9: 25.5 -0.07754871     24
## 10: 28.5 -0.08946090     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6916  -0.1174  -0.0147   0.1402   0.8603  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03694    0.02141  -1.725    0.085 .  
## timeNorm     0.01685    0.03339   0.505    0.614    
## obj.diff    -0.21183    0.02890  -7.329 7.21e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06282483)
## 
##     Null deviance: 42.611  on 626  degrees of freedom
## Residual deviance: 39.203  on 624  degrees of freedom
## AIC: 49.179
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.1754386     0.2503670 -0.08390482 114 1.7e-07 ***
##  2:      4.5      0.3934837     0.5375232 -0.12642807  57 7.7e-06 ***
##  3:      7.5      0.3558897     0.5031632 -0.10772684  57 7.4e-07 ***
##  4:     10.5      0.4010025     0.5366569 -0.10986449  57 3.5e-07 ***
##  5:     13.5      0.3333333     0.4810470 -0.12968997  57 5.7e-06 ***
##  6:     16.5      0.3082707     0.4851906 -0.14622864  57 1.6e-07 ***
##  7:     19.5      0.3433584     0.4934283 -0.11261380  57 4.9e-06 ***
##  8:     22.5      0.3809524     0.5224507 -0.12657265  57 8.3e-06 ***
##  9:     25.5      0.4385965     0.5088421 -0.07721547  57   0.0098 **
## 10:     28.5      0.3634085     0.4899874 -0.10235022  57 0.00052 ***
##     time  error.diff shapes
##  1:  1.5 -0.08390482     24
##  2:  4.5 -0.12642807     24
##  3:  7.5 -0.10772684     24
##  4: 10.5 -0.10986449     24
##  5: 13.5 -0.12968997     24
##  6: 16.5 -0.14622864     24
##  7: 19.5 -0.11261380     24
##  8: 22.5 -0.12657265     24
##  9: 25.5 -0.07721547     24
## 10: 28.5 -0.10235022     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6899  -0.3042   0.1314   0.2349   0.4789  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.327848   0.039949   8.207  1.3e-15 ***
## timeNorm    -0.004351   0.043288  -0.101     0.92    
## obj.diff    -0.633947   0.046829 -13.537  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1078885)
## 
##     Null deviance: 87.210  on 626  degrees of freedom
## Residual deviance: 67.322  on 624  degrees of freedom
## AIC: 388.23
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4849624     0.5281359 -0.04157421 114     0.27 :(
##  2:      4.5      0.6290727     0.8386323 -0.19425581  57 1.2e-05 ***
##  3:      7.5      0.6190476     0.7417870 -0.11473387  57     0.011 *
##  4:     10.5      0.6215539     0.6601240 -0.04393235  57      0.4 :(
##  5:     13.5      0.5914787     0.6721386 -0.09528694  57      0.08 .
##  6:     16.5      0.6516291     0.7262138 -0.06587885  57     0.074 .
##  7:     19.5      0.5463659     0.6300694 -0.07167203  57     0.18 :(
##  8:     22.5      0.5012531     0.6130500 -0.08720494  57      0.04 *
##  9:     25.5      0.5162907     0.5683615 -0.05218842  57     0.43 :(
## 10:     28.5      0.5112782     0.5260374 -0.02478075  57     0.47 :(
##     time  error.diff shapes
##  1:  1.5 -0.04157421     16
##  2:  4.5 -0.19425581     24
##  3:  7.5 -0.11473387     24
##  4: 10.5 -0.04393235     16
##  5: 13.5 -0.09528694     16
##  6: 16.5 -0.06587885     16
##  7: 19.5 -0.07167203     16
##  8: 22.5 -0.08720494     24
##  9: 25.5 -0.05218842     16
## 10: 28.5 -0.02478075     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70616  -0.25599   0.03072   0.24287   0.63940  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.16277    0.02810   5.793 1.03e-08 ***
## timeNorm     0.08214    0.03854   2.132   0.0334 *  
## obj.diff    -0.46349    0.03912 -11.849  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09911969)
## 
##     Null deviance: 86.496  on 725  degrees of freedom
## Residual deviance: 71.664  on 723  degrees of freedom
## AIC: 387.2
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n     pval
##  1:      1.5      0.3008658     0.2872390 -0.008624106 132  0.87 :(
##  2:      4.5      0.4826840     0.6170103 -0.137094482  66 0.001 **
##  3:      7.5      0.5108225     0.5160840 -0.023124603  66  0.57 :(
##  4:     10.5      0.5021645     0.5385434 -0.046324082  66  0.32 :(
##  5:     13.5      0.4783550     0.4967963 -0.003628038  66  0.93 :(
##  6:     16.5      0.4740260     0.4372192  0.024297695  66  0.57 :(
##  7:     19.5      0.4393939     0.4426848 -0.025355834  66  0.65 :(
##  8:     22.5      0.4047619     0.3416750  0.045008506  66  0.24 :(
##  9:     25.5      0.3896104     0.3271059  0.067289348  66  0.15 :(
## 10:     28.5      0.4199134     0.3367055  0.068892304  66  0.12 :(
##     time   error.diff shapes
##  1:  1.5 -0.008624106     16
##  2:  4.5 -0.137094482     24
##  3:  7.5 -0.023124603     16
##  4: 10.5 -0.046324082     16
##  5: 13.5 -0.003628038     16
##  6: 16.5  0.024297695     16
##  7: 19.5 -0.025355834     16
##  8: 22.5  0.045008506     16
##  9: 25.5  0.067289348     16
## 10: 28.5  0.068892304     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5917  -0.1870  -0.1249   0.2308   0.7379  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15007    0.02964   5.063 5.72e-07 ***
## timeNorm    -0.05160    0.04248  -1.215    0.225    
## obj.diff    -0.49282    0.04761 -10.351  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08574876)
## 
##     Null deviance: 54.225  on 527  degrees of freedom
## Residual deviance: 45.018  on 525  degrees of freedom
## AIC: 206.45
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n    pval
##  1:      1.5      0.2514881     0.2301605 -0.04128074 96 0.29 :(
##  2:      4.5      0.3839286     0.4760184 -0.09387526 48 0.084 .
##  3:      7.5      0.3571429     0.4340736 -0.07703553 48 0.12 :(
##  4:     10.5      0.3422619     0.3952495 -0.06600414 48  0.05 .
##  5:     13.5      0.3363095     0.3804299 -0.04404735 48 0.38 :(
##  6:     16.5      0.2916667     0.3328032 -0.05676110 48 0.24 :(
##  7:     19.5      0.2380952     0.2159761 -0.01912010 48  0.8 :(
##  8:     22.5      0.2440476     0.2550491 -0.02616089 48 0.62 :(
##  9:     25.5      0.2232143     0.2490444 -0.04238278 48 0.27 :(
## 10:     28.5      0.1666667     0.1639436 -0.03508540 48 0.12 :(
##     time  error.diff shapes
##  1:  1.5 -0.04128074     16
##  2:  4.5 -0.09387526     16
##  3:  7.5 -0.07703553     16
##  4: 10.5 -0.06600414     16
##  5: 13.5 -0.04404735     16
##  6: 16.5 -0.05676110     16
##  7: 19.5 -0.01912010     16
##  8: 22.5 -0.02616089     16
##  9: 25.5 -0.04238278     16
## 10: 28.5 -0.03508540     16